# 생산최적화 ortools로 바꾸기

import pandas as pd
import numpy as np
from itertools import product
from ortools.linear_solver import pywraplp

df_m = pd.DataFrame({'원료1':[1, 2],
                    '원료2':[4, 4],
                    '원료3':[3, 1]},
                    index = ['제품1', '제품2'])   #index 지정
df_p = pd.DataFrame({'이익':[5, 4]},
                    index = ['제품1', '제품2'])   #index 지정
df_s = pd.DataFrame({'원료1':[40],
                    '원료2':[80],
                    '원료3':[50]},
                    index = ['재고'])   #index 지정
df_plan = pd.DataFrame({'생산량':[16, 0]},
                    index = ['제품1', '제품2'])   #index 지정

def product_plan(df_profit, df_plan):
    profit = 0
    for i in range(len(df_profit.index)):
        profit += df_profit.iloc[i][0] * df_plan.iloc[i][0] #이익 * 생산량 
    return profit
    
print('이전 계획 총이익: ', product_plan(df_p, df_plan))

# Create the mip solver with the SCIP backend.
solver = pywraplp.Solver.CreateSolver('SCIP')
if not solver:
    print('Not Sovler')

infinity = solver.infinity()
v2 = {}
for i in range(len(df_p)):
    v2[i] = solver.IntVar(0, infinity, 'v%i' % i)
print('Number of variables =', solver.NumVariables())

objective = solver.Objective()
for i in range(len(df_p)):
    objective.SetCoefficient(v2[i], int(df_p.iloc[i]))
objective.SetMaximization()

for j in range(len(df_m.columns)): #j: 원료 0,1,2 / i: 제품 0, 1
    constraint = solver.Constraint(0, int(df_s.iloc[:, j]))
    for i in range(len(df_p)):
        constraint.SetCoefficient(v2[i], int(df_m.iloc[i, j]))

status = solver.Solve()
if status == pywraplp.Solver.OPTIMAL:
    print('Objective value =', int(solver.Objective().Value()))
    for i in range(len(v2)):
        print(v2[i].name(), ' = ', v2[i].solution_value())
    print()
    print('Problem solved in %f milliseconds' % solver.wall_time())
    print('Problem solved in %d iterations' % solver.iterations())
    print('Problem solved in %d branch-and-bound nodes' % solver.nodes())
else:
    print('The problem does not have an optimal solution.')